Waseem Ismaeel
Post Doc Fellow
Waseem Ismaeel is a Postdoctoral Research Fellow at Shoolini University. He is also an academic instructor specialising in Geographic Information Systems (GIS), Remote Sensing (RS), and spatial modeling, with a strong focus on urban dynamics, land use/land cover (LULC) change analysis, and sustainable urban development.
He holds a PhD in Civil Engineering with a specialisation in GIS and Remote Sensing, where his doctoral research focused on integrating advanced geospatial technologies with artificial intelligence for urban growth simulation and environmental assessment.
His research lies at the intersection of geospatial science, machine learning, and urban climate studies. His work emphasises the development of hybrid modeling frameworks, including Cellular Automata (CA), Artificial Neural Networks (ANN), and Multi-Criteria Decision-Making (MCDM) approaches such as AHP, BWM, and SWARA, to analyse and predict urban expansion patterns. He has extensive experience in processing satellite imagery, implementing deep learning models for built-up area extraction, and utilising cloud platforms such as Google Earth Engine for large-scale geospatial analysis.
He has contributed to multiple peer-reviewed publications in Scopus and SCIE-indexed journals, focusing on urban growth modeling, suitability analysis, and spatial decision support systems. His recent work also explores the interaction between urban expansion, transportation networks, and local climate extremes, aligning with global sustainability frameworks such as the United Nations Sustainable Development Goals (SDG 11).
Ismaeel is proficient in Python-based geospatial analysis, machine learning model development, and advanced spatial statistics. His interdisciplinary expertise enables him to bridge engineering, environmental science, and data-driven urban planning.
Publications
Research Interests:
- Urban Growth Modeling and Simulation.
- Land Use/Land Cover (LULC) Change Detection.
- GIS and Remote Sensing Applications.
- Machine Learning and Deep Learning in Geospatial Analysis.
- Cellular Automata and Spatial Modeling.
- Multi-Criteria Decision-Making (AHP, BWM, SWARA).
- Urban Climate and Heat Island Studies.
- Sustainable Urban Development and SDG Assessment.
- Google Earth Engine and Big Geospatial Data.
Publications:
- Ismaeel, W. A., & J. Satish Kumar (2023). BWM-fuzzy and bivariate analysis-based decision support system for urban development site in Latakia, Syria. GeoJournal, 88, 4493–4503. https://doi.org/10.1007/s10708-023-10873-y
- Ismaeel, W. A., & J. Satish Kumar (2024). Land suitability analysis of new urban areas using MIF-AHP and bivariate analysis methods in Latakia, Syria. Environment, Development and Sustainability, 26, 8087–8101. https://doi.org/10.1007/s10668-023-03878-7
- Ismaeel, W. A., & J. Satish Kumar (2024). An approach for built-up area extraction using different indices and deep neural network (DNN) model. Infrared Physics and Technology, 142 https://doi.org/10.1016/j.infrared.2024.105558
- Ismaeel, W. A., & J. Satish Kumar (2024). Analytical Hierarchy Process for Land Suitability Analysis of Urban Growth in Latakia, Syria. In: ICC IDEA 2023, Lecture Notes in Civil Engineering, vol. 398. Springer. https://doi.org/10.1007/978-981-99-6229-7_16
- Ismaeel, W. A., & J. Satish Kumar (2025). Analysis and Prediction of Land Use Land Cover Dynamics for Northwest Syria Using Artificial Neural Network Model. Earth Systems and Environment, 9(3), 2257–2276. https://doi.org/10.1007/s41748-025-00702-2
Contact Details:
Email: waseemismaeel@shooliniuniversity.com
Google Scholar profile: https://scholar.google.com/citations?hl=en&user=zEUT3TkAAAAJ